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Journal of Research on Trade, Management and Economic Development VOLUME 3, ISSUE 2(6)/2016

DETERMINANTS OF INFLATION IN BALI

Yeyen Komalasari, Doctoral Candidate of Management Science Universitas Udayana,

Lecturer, Universitas Dhyana Pura, Bali, Indonesia

E -mail: [email protected]

Christimulia Purnama Trimurti, Lecturer, Lecturer at Universitas Dhyana Pura, Bali, Indonesia

E -mail: [email protected]

JEL classification: E31, P44

Abstract

Inflation in Bali is high compared with other countries. This study is an investigation of the determinants of

inflation in Bali for the period 2000-2014. To analyse the influence of the determinant factors of inflation authors

have applied regression analysis by Eviews 6.

Our study has found that such factors, as foreign exchange, bank interest rate, the size of government

expenditure, the size of public expenditure have no influence on inflation.

Researchers have suggested the government to focus not only on monetary policy but also on other

techniques and policies that would encourage the supply of goods, would avoid deficit and would contribute to

maintaining stable oil prices in Bali.

Keywords: inflation, exchange rate, government expenditure, interest.

1. Introduction

Bali is a famous tourist destination in the world with the level of foreign tourist arrivals with an

average growth from 2010-2014 year amounted to 9.6 percent (BPS Bali, 2015) [24]. Bali rely on

the tourism sector to improve the welfare of the community through tourist visits, performing arts

and culture, and art products in export sales. Bali has only relatively small area of 5636.66 km2

with a population in 2014 amounted to 4.1049 million people, but do not have the economic

resources to rely on to prop up the economy of Bali. Bali Tourism sector is the primary key that

causes the Bali regional income continued to rise and unemployment declines significantly.

Bali is very dependent on the economic resources that come from outside Bali that are at risk of

high inflation. In a period of 15 years (2000-2014), the inflation that occurred in Bali between 3.8

% and 12.49 % (BPS Bali, 2015) [24]. The economic growth the last 15 years more dominant low

compared with inflation of between 3.05 % and 6.72 %. Fluctuations in the exchange rate against

the US dollar exchange rate during the last 15 years that tend to depreciate the exchange rate could

potentially lead to higher inflation. Trigger high inflation in Bali foreseeable from government

spending in the last 15 years has increased from 0.10 % to 125.72 %. Changes in public

expenditures of Bali in the last 15 years between 2 % up to 195 % can be expected to trigger

inflation driver in Bali. Provision of lending rates in the last 15 years has the tendency to decrease

from 18.43 % per to 12.79 % per year. Bank Indonesia as the Central Bank has made an effort to

control inflation by setting interest rates.

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Bali has a Regional Inflation Control Team under the control of Bank Indonesia to control

inflation in Bali. Inflation Control Team use an Inflation Targeting Framework (ITF). Inflation

Control Team consists of various local governance institutions that continue to monitor and try to

react quickly in controlling inflation in Bali. Inflation that occurred from 2000-2014 high

likelihood that it is very interesting to do research on the determinants of inflation that occurred in

Bali.

2. Literature Review on Determinant of Inflation

The quantity theory was discussed by the famous 18th-century philosopher and economist David

Hume and has been advocated more recently by the prominent economist Milton Friedman. This

theory can explain moderate inflations, such as those we have experienced in the United States, as

well as hyperinflations. Hume and his contemporaries suggested that economic variables should be

divided into two groups. The first group consists of nominal variables—variables measured in

monetary units. The second group consists of real variables—variables measured in physical units.

The real interest rate is, after all, a real variable. For the real interest rate not to be affected, the

nominal interest rate must adjust one-for-one to changes in the inflation rate. Thus, when the Fed

increases the rate of money growth, the long-run result is both a higher inflation rate and a higher

nominal interest rate. This adjustment of the nominal interest rate to the inflation rate is called the

Fisher effect, after economist Irving Fisher (1867–1947), who first studied it (Mankiw, 2009) [15].

Before the advent of rational expectations in monetary economics, Milton Friedman and Edmund

Phelps proposed, in the late 1960s, the expectations augmented Phillips curve, which showed that

if inflation rose above expected inflation, then output and employment would rise above normal

(Taylor in Siegfried, 2010) [21].

Laryea & Sumaila (2001) [13] estimates an inflation equation for Tanzania based on quarterly

data, for the period 1992:1 to 1998:4. The results from the econometric regression analysis shows

that inflation in Tanzania, either in the short run or the long run, is influenced more by monetary

factors and to a lesser extent by volatility in output or depreciation of the exchange rate.

Kim (2001) [12] using co-integration and error models, this paper analyses the relative impacts of

the monetary, labour and foreign sectors on Polish inflation from 1990 to 1999. We use a

structural system approach in which co-integration relationships are used to derive deviations from

steady-state levels. The results suggest that the labour and external sectors dominated the

determination of Polish inflation during the above period, but their effects have been opposite

since 1994. The appreciation of the domestic currency contributed to reducing inflation, while

excessive wage increases prevented inflation from decreasing to a lower level. The monetary

sector appears not to have exerted influence on inflation, suggesting monetary policy has been

passive.

Andersson, et al (2009) [2] examined the determinants of inflation differentials and price levels

across the euro area countries. Dynamic panel estimations for the period 1999-2006 show that

inflation differentials are primarily determined by cyclical positions and inflation persistence. The

persistence in inflation differentials appears to be partly explained by administered prices and to

some extent by product market regulations. In a co-integrating framework we find that the price

level of each euro area country is governed by the levels of GDP per capita.

Greenidge dan DaCosta (2009) [8] examines the determinants for inflation in the Caribbean. This

paper uses annual data over the period 1970 to 2006 for Barbados, Guyana, Jamaica and Trinidad



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and Tobago. Data are obtained from the International Financial Statistics Database, Economic and

Financial Statistics publications (Barbados) and Annual Statistical Digest publications from

several central banks in the Caribbean. The findings indicate that the determinants for inflation in

the Caribbean are both cost-push and demand-pull.

Khan & Gill (2010) [11] focuses on the determinants of inflation in Pakistan using four price

indicators, i.e. CPI, WPI, SPI, and GDP Deflator for the long-run (time period of 1971-72 to 2005-

06). It is found that depreciation of exchange rate and increase in the value of imports has

contributed shooting up of CPI, WPI, SPI and GDP deflator. The support prices of sugar-cane,

rice, wheat, and cotton (collectively) have affected all the indicators positively, however, the

support price of wheat independently has affected only GDP deflator. Expectation effect has also

contributed positively towards all the indicators.

Bayo (2011) [6] investigates the determinants of inflation in Nigeria between 1981 and 2003. The

Nigerian economy had faced with inflationary trends over the years and the various government

policies to deal with it eluded long-term solution needed to bring about increased living standard

of the Nigerian citizenry. Hence, the need for an investigation into the multi-dimensional and

dynamic factors that affect inflation with the view to make appropriate recommendations to

curbing it. From the study, it was revealed that explanatory variables (fiscal deficits, money

supply, interest and exchange rates) significantly and positively impacted on the rate of inflation in

Nigeria during the period under review.

Bashir, et al (2011) [5] focuses to examine demand side and supply side determinants of inflation

in Pakistan on economic and econometric criterion and also to investigate causal relationships

among some macroeconomic variables. For that purpose, study has undertaken time series data for

the period from 1972 to 2010. Long run and short run estimates have been investigated using

Johansen Co-integration and Vector Error Correction approach. Causal relationship have been

observed using Granger causality test. The findings of the study reveal that in the long run

consumer price index has found to be positively influenced by money supply, gross domestic

product, imports and government expenditures, on the other side government revenue is reducing

overall price level in Pakistan.

Imimole B & Enoma A (2011) [10] examined the impact of exchange rate depreciation on

inflation in Nigeria for the period 1986–2008, using Auto Regressive Distributed Lag (ARDL) Co-

integration Procedure. The research found that exchange rate depreciation, money supply and real

gross domestic product are the main determinants of inflation in Nigeria, and that Naira

depreciation is positive, and has significant long-run effect on inflation in Nigeria.

Aurangzeb & Haq (2012) [4] investigates the determinants of inflation in Pakistan. The data used

in this study were collected from the period of 1981 to 2010. Unit root test confirms the stationary

of all variables at first difference. The multiple regression analysis technique is used to identify the

significance of different factors. Results indicate that gross domestic production is having negative

relationship with inflation, while exchange rate, interest rate, fiscal deficit and unemployment have

positive relationship inflation. It is recommended that the policy makers should critically evaluate

and analyze the exchange rate, remittances, gross domestic production and foreign direct

investment on continuous basis to reduce the trade deficit.

Tafti (2012) [23] measures and analyzes the determinants of inflation in Islamic Republic of Iran.

After briefly reviewing the theoretical background, we use Johansen and Juselius maximum

likelihood method. Additionally, we use the VAR method. For this purpose, Impulse Response

Functions (IRF) and Forecast Error variance Decomposition (FEVD) are also used. Our analysis is



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based on time serious quarterly data from 1971:1-2005:4 and our results show that the response of

the consumer price index (CPI) to shock in GDP is too weak and the response of CPI to shocks in

import price index and liquidity is initially positive.

Ndidi (2013) [17] employs time series econometric technique, using Augmented Dickey Fuller

(ADF) and Philips Perron (PP) tests (test for ascertaining the presence of unit root and stationarity

of the series) and co-integration tests, which is used to explore the presence of long-run

relationship amongst the series. It uses yearly data between 1970 and 2010, and found that

expected inflation, measured by the lagged term of inflation, money supply, significantly

determine inflation, while trade openness, capturing the tendencies of imported inflation, income

level, exchange rate and interest rate are found not to be significant with all showing signs that

conform with a priori in the short run in Nigeria.

Salem, et al (2013) [20] examines the impact of unemployment, exchange rate, gross domestic

product, interest rate and fiscal deficit on inflation rate in Pakistani economy Annual data collected

from Asian Development Bank and State Bank of Pakistan website from1990-2011 for Pakistan

has been used. Regression analysis has been employed through SPSS statistical package. The

result shows that there is negative relationship between inflation rate with unemployment and

fiscal deficit while positive relationship is examined between inflation rate with exchange rate,

GDP and interest rate in Pakistan.

Ong & Chang (2013) [18] identify the macroeconomic determinants of house prices in Malaysia

from year 2000 until middle of 2012. The study also explores the relationship between the

characteristics and house price then will forecast the house price index for the next 3 years. There

are total of three macroeconomic variables, such as inflation rate, gross domestic product rate and

income increment rate were taken into study with the house price index. The design of this study

comprises of 50 secondary data of each variables from year 2000 until middle of year 2012 which

are in quarterly basis. In the process of findings, there are few types of analysis which were tested

by using SPSS Version 20.0, there are Pearson correlation coefficient, multiple regression

analysis, multicollinearity statistics and finally scenario analysis to forecast the future trend of

house price index in next three years. The result indicates that there are not all predictors in this

study are significantly related to house price, but only gross domestic product rate is significant

determinants of the movement of house price index.

Hossain & Islam (2013) [9] examine the determinants of inflation using the data from 1990 to

2010 in Bangladesh. The Ordinary Least Square (OLS) method has been used to explain the

relationships. The empirical results show that money supply, one year lagged value of interest rate

positively and significantly affect inflation. Results also indicates that one year lagged value of

money supply and one year lagged value of fiscal deficit significantly and negatively influence

over inflation rate. There is an insignificant relationship between interest rate, fiscal deficit and

nominal exchange rate. The explanatory variables accounted for 87 percent of the variation of

inflation during the study period.

Sola & Peter (2013) [22] examines money supply and inflation rate in Nigeria. Secondary data that

ranged from 1970 to 2008 were sourced from the CBN Statistical Bulletin. The study used Vector

Auto Regressive (VAR) model. The stationary properties of the model were also explored. The

results revealed that money supply and exchange rate were stationary at the level while oil revenue

and interest rate were stationary at the first difference. Results from the causality test indicate that

there exists a unidirectional causality between money supply and inflation rate as well as interest



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rate and inflation rate. The causality test runs from money supply to inflation, from the interest rate

to inflation and from interest rate to money supply.

Mirchandani (2013) [16] examines the relationship between Exchange rate and Macro-economic

variables such as, interest rate, Balance of trade, Inflation rate, Foreign Direct Investment, GDP

etc. which have been analyzed with the help of statistical tool. The research is based on secondary

data, to compile the report with some variables twenty years annual data for the period of 1991 to

2010 were collected. It has been found that Exchange rates is correlation with many variables such

as interest rate, inflation rate & GDP Growth rate in either direct or indirect manner in India.

Lim & Sek (2015) [14] examines factors affecting inflation in two groups of countries (high

inflation group and low inflation group) using annual data from 1970 to 2011. An Error Correction

Model based on the Autoregressive Distributed Lag (ARDL) modeling has been used to explain

the short run and long run impacts of each variable on inflation. The results respectively indicate

that GDP growth and imports of goods and services have the significant long run impact on

inflation in low inflation countries. Results also indicate that money supply, national expenditure

and GDP growth are the determinants of inflation which impose long run impact on inflation in

high inflation countries. In the short run likewise, none of the variables is found to be significant

determinants in high inflation countries.

Ashwani (2014) [3] identifies the key determinants of inflation in India. Meanwhile it tries to

empirically investigate the recently boasted argument that India’s inflation is mainly attributed to

higher purchasing power of the people due to better economic growth amid increased social sector

spending. To serve the purpose, annual time series data is utilized ranging from 1981 to 2011. Co-

integration method is used to identify the long-run relationship followed by error correction model

for short-run relationship among the inflation and other macro-economic indicators. It is found that

there is presence of long-run relationship between inflation, money supply, private and social

spending and exchange rate in India. Money supply, exchange rate and private final consumption

expenditure contribute the inflation significantly.

Alexander, et al (2015) [1] investigated the main determinants of inflation in Nigeria for the period

1986 – 2011. The Augmented Dickey-Fuller unit root statistics test revealed that all the variables

are stationary after first and second difference at 5% level of significance. The co-integration result

reveals long-run equilibrium relationship between the rate of inflation and its determinants. The

Granger causality test revealed evidence of a feedback relationship between inflation and its

determinants. The estimated VAR result showed that fiscal deficits, exchange rate, import of

goods and services, money supply and agricultural output have a long run influence on inflation

rate in Nigeria. Only lending rate influenced inflation in the short and long run horizon. The

variance decomposition and impulse response results show that “own-shocks” were significantly

responsible for the variation and innovations in all the variables in the equation. Obviously,

inflation in Nigeria is fiscal and monetary policy influence. While this study discourages excessive

waste of public funds through fiscal deficit, it recommends that the monetary authority should

encourage a lending rate policy that promotes investment as well as retention of a desired level of

money supply and interest rates that reduce inflation rate in Nigeria.

Ghumro & Memon (2015) [7] examine the main sources of inflation in the economy of Pakistan

using an autoregressive distributed lag model for the period from 1980 to 2012. Findings of this

study reveal that the one percent rise in the long run money supply, exchange rate, total reserve,

and the gross national expenditure change inflation by 0.16, 2.12, 0.36, and 1.78 % points

respectively. The Error Correction model with negative sign remains statistically significant with



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approximate 81% speed of adjustment to restore the equilibrium in the long run, which shows the

quick convergence.

Paudyal (2015) [19] examines short term and long term effects of the macroeconomic variables on

the inflation in Nepal during 1975-2011. The variables considered are budget deficits, Indian

prices, broad money supply, exchange rate and real GDP. The regression results from Wickens-

Breusch Single Equation Error Correction model suggest that all variables considered are

significant in long run implying that these variables are the determinants of inflation in Nepal.

3. Research Methodology

This study uses Bali as research objects. The data used comes from the time series data for 15

years beginning in 2000-2014. Variable data were taken from BPS Bali and Bank Indonesia.

Variable data were analyzed by Eviews 6 to test the effect of independent variables with the

dependent variable. There are four variables used in this study to find the determinants of inflation

in Bali. Inflation variable is used as the dependent variable while the independent variables are

exchange rates, interest rates, government spending, and public spending. The dependent variable

used in this study is inflation (Y). The models in this study are as follows:

Y = α + β1X1 + β2X2 + β3X3 + β4X4, (1)

where:

X1 = rupiah against the US dollar in Rupiah;

X2 = the central bank interest rate in percent;

X3 = government spending in Rupiah;

X4 = public expenditure in Rupiah.

Research Hypothesis:

The exchange rate significantly affect inflation

Central bank interest rate significantly to inflation

Government spending significantly affect inflation

Public spending significantly affect inflation.

4. Results and Discussion

1. Serial Correlation Test

To detect the presence of serial correlation by comparing the count value X2 with X2 table,

namely:

a. If the value of X2 count> X2 table (5.99), then the hypothesis that the model is free of the

problem of serial correlation is rejected

b. If the value of X2 count <X2 table (5.99), then the hypothesis that the model is free of the

problem of serial correlation accepted

Value Obs * R-squared (X2 count value) 3.409 <5.99 X2 table so that the free model of serial

correlation problem. Value Obs * R-squared (X2 count value) can be found in appendix 1.

2. Normality Test Data

To detect whether the residual distribution is normal or not by comparing the value of Jarque Bera

(JB) with X2 table is:

a. If the value JB> X2 table, then the residual distribution is not normal

b. If the value JB <X2 table, then the residual normal distribution



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Analysis of the output, that JB value of 0.592 <5.99 X2 table it can be concluded that the residuals

were normally distributed. JB value can be found in appendix 2.

3. Test linearity

To detect whether or not a linear model by comparing the value of the F-Statistic with F-Table are:

a. If the value of F-Statistic> F-table, then the hypothesis that the linear model is rejected

b. If the value of F-Statistic <F-table, then the hypothesis that the linear model is accepted

Analysis of output results, that the value of F-Statistic amounted to 0.366 <3.49 F-table so that the

linear model can be accepted. F-Statistic value can be found in Appendix 3.

4. Test Multicollinearity

Stages testing via Eviews 6 program with partial correlation approach with the following steps.

1) The regression equation:

Y = a0 + a1X1 + a2X2 + a3X3 + a4X4

2) Estimates for regression:

X1 = b0 + b1X2 + b2X3 + b3X4

X2 = b0 + b1x1 + b2X3 + b4X4

X3 = b0 + b1x1 + b2X2 + b3X4

X4 = b0 + b1X2 + b2X2 + b3X3

Conditions:

If the value of R21> R211, R212, R213, R214 then the model is not found their multicolinearity

If the value of R21 <R211, R212, R213, R214 then the model is found some multicolinearity

The analysis of output results show that R21 = 0.347 <R211 = 0.566, = 0.498713 R212, R213 =

0944, R214 = 0.933 so the model found their multicolinearity. The R2 can be found in appendix 4.

5. Test Heteroskedasitas

Heteroskedasitas test using white test with the following conditions:

If the value of X2 count (value Obs * R squared)> value X2 table it can be concluded that the

model does not pass the test heterokedasitas

If the value of X2 count (Obs * R squared value) <value X2 table it can be concluded that the

model passes the test heterokedasitas

X2 count value 0.356 <5.99 value X2 table it can be concluded that the model passes the test

heterokedasitas. Value Obs * R-squared (X2 count value) can be found in Appendix 5.

6. Multiple Linear Regression Test Results

Multiple regression equation as follows.

Y = -4.062860 + 0.001441X1 + 0.058886X2 + 3.87E-13X3 -1.10E-07X4

The results showed that the X1 does not significantly affect Y where significant level of 0.2531

above 5%. This means that the exchange rate does not significantly affect inflation. Inflation in

Bali was not caused by the fluctuation of the rupiah against the US dollar. Values Values

significant level of research can be found in appendix 6.


above 5%. This means that interest rates do not significantly affect inflation. Inflation in Bali was

not caused by fluctuations in interest rates of the central bank. Bank Indonesia policy by playing

the interest rate banks are not able to control inflation that occurred in Bali. The value of a

significant level of research can be found in appendix 6.


above 5%. This means that government spending does not significantly affect inflation. Inflation



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in Bali is not caused by government spending. The results of this study will not make the concerns

of the government against government spending will have an impact on the increase in inflation in

Bali. The value of a significant level of research can be found in appendix 6.


above 5%. This means that public expenditure does not significantly affect inflation. Inflation in

Bali is not caused by public spending. The results of this study will not make the concerns of the

government to increase public spending on inflation. The value of a significant level of research

can be found in appendix 6.

Control of inflation that occurred in the period 2000-2014 is difficult to do by the local

government of Bali because Bali does not have the economic resources to meet community needs.

Economic resources needed by Bali such as agricultural products, animal husbandry, fisheries,

energy and mineral resources are very dependent on various Provinces. Dependence on the

economic resources to trigger the difficulty of controlling inflation as a result of excess demand

while supply of the goods less. Bali is often in a shortage of product offerings due to delays in the

supply of goods from the various Provinces that meet the needs of goods in Bali. Delays in the

supply of goods to be one of the biggest triggers of inflation in Bali. Delays items that are often

caused by bad weather and poor transport system.

During the years 2000-2014 have been frequent increase in oil prices in response to changes in

world oil prices. The increase in oil prices which often happens to be the trigger inflation due to

higher transport costs and the price of electricity tariffs. Energy resources for the province of Bali

from Java Island and the determination of electricity rates are determined by the Government of

Indonesia. Provincial Government of Bali has been several times proposed the application of

alternative energy geothermal, but the proposal of the government was rejected by the majority of

Balinese society. Their energy dependence of Java makes the high cost of electricity is borne by

the people of Bali so it is pushing up inflation.

5. Conclusions and Recommendations

Bali has a big chance of tourism economic base the rise in inflation. Values T Statistic on a

variable exchange rate of 1.212723 to 0.2531 probability gives the sense that the exchange rate has

no effect on inflation in Bali. Increasing the number of foreign tourists visiting Bali does not have

an impact on inflation in Bali.

The central bank has attempted to conduct monetary control by changing interest rates. The results

found that the value of the variable T Statistics on central bank rate of 0.190345 to 0.8528

probability gives the sense that the interest rate has no effect on inflation in Bali. The central

bank's monetary policy has not been effective in conducting monetary control in Bali.

Bali Provincial Government each year to make changes to the financing of regional development

expenditure. The study found that government spending has no effect on inflation in Bali. Value T

statistics on government expenditure variable probability of 0.158907 to 0.8769 gives the sense

that government spending has no effect on inflation in Bali. Inflation in Bali is not caused by

government spending.

Changes in public expenditures of Bali in the last 15 years between 2 percent up to 195 percent

very fluctuating. The study found that public expenditure does not affect the inflation in Bali.



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Value T Statistics on public expenditure variables for -0.387646 with probability 0.7064 gives the

sense that public spending has no effect on inflation in Bali. Public expenditure is not to be

triggered from the high inflation in Bali.

Recommendations.

Local governments should not just concentrate on making monetary policy in controlling inflation,

but keep other economic policies that are more technical like to maintain the supply of goods in

order to avoid shortages and maintain the stability of oil prices in Bali.

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18. ONG TZE SAN & CHANG YEE SHAN. Macroeconomic Determinants Of Malaysian Housing Market. 2013,

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17%2B.pdf. 2013.

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NRB Economic Review. Available: http://www.nrb.org.np/ecorev/pdffiles/vol26-2_art4.pdf.

20. SALEEM FAIZA, ET.AL. Determinants Of Inflation In Pakistan. Vol.4 No.9, Januari 2013. Interdisciplinary

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21. SIEGFRIED JOHN J. Better Living Through Economics. Harvard University Press. 2010.

22. SOLA OLORUNFEMI & PETER ADELEKE. Money Supply and Inflation in Nigeria: Implications for National

Development. 2013, Vol.4 pp. 161-170. Modern Economy Journal. Available:

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23. TAFTI FARIBA CHAVOSHZADEH. Determinants of Inflation in Islamic Republic of Iran. Vol. 3 No. 6;

[Special Issue -March 2012]. International Journal of Business and Social Science. Available:

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24. Badan Pusat Statistik Bali (BPS Bali). 2000-2015. Bali Dalam Angka. BPS Provinsi Bali Available:

https://bali.bps.go.id/

Appendix 1. Serial Correlation Test Appendix 2. Normality Data Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.176856 Prob. F(2,8) 0.3564

Obs*R-squared 3.409953 Prob. Chi-Square(2) 0.1818

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Sample: 1 15

Included observations: 15

Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob.

X1 -0.000961 0.001359 -0.706828 0.4997

X2 0.558381 0.478095 1.167929 0.2765

X3 1.17E-12 2.53E-12 0.462239 0.6562

X4 1.53E-08 1.39E-07 0.109862 0.9152

C 1.204200 10.50117 0.114673 0.9115

RESID(-1) -0.776157 0.506051 -1.533752 0.1636

RESID(-2) -0.399165 0.415802 -0.959987 0.3652

R-squared 0.227330 Mean dependent var 1.11E-16

Adjusted R-

squared -0.352172 S.D. dependent var 2.368724

S.E. of regression 2.754422 Akaike info criterion 5.169017

Sum squared

resid 60.69474 Schwarz criterion 5.499441

Log likelihood -31.76763 Hannan-Quinn criter. 5.165498

F-statistic 0.392285 Durbin-Watson stat 1.538996

Prob(F-statistic) 0.864742

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

Series: Residuals

Sample 1 15

Observations 15

Mean 1.11e-16

Median 0.049848

Maximum 3.877452

Minimum -3.896863

Std. Dev. 2.368724

Skewness 0.025307

Kurtosis 2.027701

Jarque-Bera 0.592454

Probability 0.743619

0

1

2

3

4

-4 -3 -2 -1 0 1 2 3 4

Series: Residuals

Sample 1 15

Observations 15

Mean 1.11e-16

Median 0.049848

Maximum 3.877452

Minimum -3.896863

Std. Dev. 2.368724

Skewness 0.025307

Kurtosis 2.027701

Jarque-Bera 0.592454

Probability 0.743619

Appendix 3. Linearity test

Ramsey RESET Test:


Log likelihood ratio 0.598646 Prob. Chi-Square(1) 0.4391

Test Equation:

Dependent Variable: Y


http://www.scirp.org/journal/PaperInformation.aspx?PaperID=29097

http://ijbssnet.com/journals/Vol_3_No_6_Special_Issue_March_2012/24.pdf


89


Sample: 1 15



X1 -0.001395 0.004842 -0.288059 0.7798

X2 -0.074140 0.387906 -0.191129 0.8527

X3 -2.11E-13 2.70E-12 -0.078106 0.9395

X4 9.02E-08 3.62E-07 0.249252 0.8088

C 11.28315 27.56678 0.409302 0.6919

FITTED^2 0.131914 0.217913 0.605352 0.5599

R-squared 0.372598 Mean dependent var 7.462667

Adjusted R-squared 0.024042 S.D. dependent var 2.931403


Sum squared resid 75.47872 Schwarz criterion 5.536898




Appendix 4. Multicollinearity Test

Y = a0 + a1X1 + a2X2 + a3X3 + a4X4

R21 = 0,347



Sample: 1 15



X1 0.001441 0.001188 1.212723 0.2531

X2 0.058886 0.309366 0.190345 0.8528

X3 3.87E-13 2.43E-12 0.158907 0.8769

X4 -1.10E-07 1.41E-07 -0.781757 0.4525

C -4.062860 10.48084 -0.387646 0.7064








X1 = b0 + b1X2 + b2X3 + b3X4

R211 = 0,566

Dependent Variable: X1


Sample: 1 15



C 8101.370 1053.838 7.687495 0.0000

X2 50.69777 77.02227 0.658222 0.5239

X3 1.20E-09 4.99E-10 2.412320 0.0345

X4 -4.16E-05 3.35E-05 -1.241484 0.2402




90



Sum squared resid 5566921. Schwarz criterion 16.38433




X2 = b0 + b1X1 + b2X3 + b4X4

R212 = 0.498713



Sample: 1 15



C 6.431748 10.02898 0.641317 0.5345

X1 0.000747 0.001136 0.658222 0.5239

X3 -4.22E-13 2.37E-12 -0.178346 0.8617

X4 -1.08E-07 1.34E-07 -0.807872 0.4363








X3 = b0 + b1X1 + b2X2 + b3X4

R213 = 0.944



Sample: 1 15



C -2.48E+12 1.06E+12 -2.331883 0.0397

X1 2.87E+08 1.19E+08 2.412320 0.0345

X2 -6.83E+09 3.83E+10 -0.178346 0.8617

X4 52602.16 7366.348 7.140873 0.0000

R-squared 0.944862 Mean dependent var 1.74E+12

Adjusted R-squared 0.929824 S.D. dependent var 1.31E+12

S.E. of regression 3.47E+11 Akaike info criterion 56.20812

Sum squared resid 1.33E+24 Schwarz criterion 56.39694




X4 = b0 + b1X2 + b2X2 + b3X3

R214 = 0,933



Sample: 1 15





91

C 35480017 19673906 1.803405 0.0988

X1 -2951.057 2377.040 -1.241484 0.2402

X2 -518846.5 642238.3 -0.807872 0.4363

X3 1.56E-05 2.19E-06 7.140873 0.0000

R-squared 0.933811 Mean dependent var 29913227

Adjusted R-squared 0.915760 S.D. dependent var 20632857

S.E. of regression 5988508. Akaike info criterion 34.27176

Sum squared resid 3.94E+14 Schwarz criterion 34.46057




Appendix 5. Heterokedasitas

Heteroskedasticity Test: White


Obs*R-squared 5.344139 Prob. Chi-Square(4) 0.2538

Scaled explained SS 1.220484 Prob. Chi-Square(4) 0.8747

Test Equation:

Dependent Variable: RESID^2


Sample: 1 15



C 0.067723 10.19249 0.006644 0.9948

X1^2 2.30E-08 1.17E-07 0.196877 0.8479

X2^2 0.033487 0.023572 1.420606 0.1859

X3^2 -1.03E-24 9.22E-25 -1.114406 0.2912

X4^2 3.45E-15 3.56E-15 0.969332 0.3552








Appendix 6.

Y = -4.062860 + 0.001441X1 + 0.058886X2 + 3.87E-13X3 -1.10E-07X4



Sample: 1 15



X1 0.001441 0.001188 1.212723 0.2531

X2 0.058886 0.309366 0.190345 0.8528

X3 3.87E-13 2.43E-12 0.158907 0.8769

X4 -1.10E-07 1.41E-07 -0.781757 0.4525

C -4.062860 10.48084 -0.387646 0.7064



92








Rezumat

Inflația în Bali este mai mare decît în alte țări. Acest studiu reprezintă o investigație a factorilor

determinanți ai inflației în Bali pentru perioada anilor 2000-2014. Pentru examinarea influenței factorilor

determinanți ai inflației autorii au utilizat analiza de regresie prin Eviews 6.

Studiul efectuat a constatat că factorii, după cum urmează: cursul valutar, rata dobînzii bancare, mărimea

cheltuielilor guvernamentale, mărimea cheltuielilor publice, nu influențează asupra inflației.

Cercetătorii au sugerat guvernului să se concentreze nu numai asupra politicii monetare, ci și asupra altor

tehnici și politici care ar încuraja livrările de bunuri, ar permite evitarea deficitului și ar contribui la menținerea

prețurilor stabile la petrol în Bali.

Cuvinte-cheie: inflație, rată de schimb, cheltuieli publice, dobîndă.

Аннотация

Инфляция в Бали значительно превышает инфляцию других стран. Данное исследование заключено в

изучении факторов, которые определяли инфляцию в Бали на период 2000-2014 г.г. Для выявления влияния

факторов, лежащих в основе инфляции, авторы применили регрессионный анализ с помощью Eviews 6.

Проведенное исследование показало, что конкретные факторы как: обменный курс, процентная

ставка банка, государственные расходы не влияют на инфляцию.

Исследователи предложили правительству сосредоточить внимание не только на денежно-

кредитную политику, но и на другие, более технические политики, способствующие росту поставок

товаров, избежанию дефицита и поддержанию стабильности цен на нефть на Бали.

Ключевые слова: инфляция, обменный курс, государственные расходы, проценты.

Received 28.03.2016

Accepted 17.05.2016

Published 30.12.2016


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